The film industry dates back to the early 1890s, when motion cameras were first invented and film production companies were being established. During this period, black and white films were screened in a dark theater room wherein the film itself ran without any sound and typically for about a minute or so. As time passed, technology improved and, as a result, films (alternatively referred to herein as “movies”) have become exponentially impressive both from a visual and an auditory stand point. The technological growth has been so explosive that scripts of potential films that could not be made due to lack of technology are now being created.
The box office, or ticket office, is now a multi-billion dollar business run by Fortune 500 film production companies. While film production companies are generally responsible for the actual production of a film, film distribution companies are generally responsible for the marketing of a film. Generally speaking, the prediction of box office performances prior to their respective release dates is relevant to both film production companies and film distribution companies. In fact, film distribution companies, or distributors, offer production deals to producers/directors, who may have production studios, but the distributor typically drives the marketing and greenlights the film production budget. Accordingly, use of the prediction method of the present invention is arguably more relevant to film distribution companies than to film production companies. However, since the benefits of the present invention are applicable to both film production companies and film distribution companies, references made herein to “film production companies,” and the like, should be construed to include “film distribution companies,” and vice versa, since the present method is actually applicable to any entity that could benefit from optimizing the budget and release date well ahead of the actual release of a film; preferably, during film production budgeting. Some films are made for reasons entirely unrelated to financial gain (i.e. profitability). For example, because Hollywood is very artistic many films are made simply because executives believe in them, even if they fail, while other films are made simply for awards recognition. While making a profit on a film is clearly beneficial, a common benchmark employed in the film industry to define whether a film is deemed a “success” or not is whether the film has reached its “break-even” point; that is, whether the film has earned enough money to at least recoup the money that was spent producing the film. For obvious reasons, the relative abilities of competing film distribution companies to optimize executive decisions concerning film budgeting and release date determination well ahead of time, and most preferably at the time of film budgeting, are relevant to their success. Accordingly, any means available to a film distribution company to enable improved worldwide success of produced films would be welcomed with open arms. In that regard, the availability of a means for improving the accuracy of film release date and budgeting predictions would be highly desirable in the film production/distribution industry. The ability to do so well before the actual release dates of films (e.g., preferably, at the time of film production budgeting) would be very beneficial.
Within the film industry, the conventionally-referenced film calendar year, e.g., when broadly describing the time of year of a film's release, is generally referenced to in relation to a particular one of the four corresponding standard calendar year seasons (i.e., spring, summer, fall/autumn, and winter). As part of the film release date/film budgeting calculation, film production/distribution companies often factor in the film calendar season (i.e. correlating to the standard calendar year season). However, determining the effect that a movie release date season, in and of itself, ultimately has on a particular film's worldwide box office performance is, for the most part, a guessing game.
Some of the antiquated models commonly relied upon are based on domestic tracking and/or foreign tracking. However, the use of tracking methods to yield revenue prediction models is known to be biased and unreliable. For example, domestic tracking models are limited in that they only attempt to predict performance for the opening weekend of a film, as opposed to predicting the total audience size over the film's entire theatrical run; thereby discounting the possibility that a film may be front-loaded (e.g., where a disproportionately high portion, such as 80%, of total ticket sales occur during the opening weekend, but then the film flops). Domestic tracking also undercounts American moviegoers who are not active in social media (e.g. older ticket buyers) and, instead, focuses on tracking the fluctuation of a film's marketing campaign, which may be reaching an unrepresentative sample of moviegoers (e.g. moviegoers who are active on certain social media platforms, diehard fans of a particular video game, etc.). Accordingly, domestic tracking does not yield a best estimate of the projected audience size for a film based upon its release date relative to its budget. For example, one week prior to the release of a film domestic tracking could reveal that a film is on track to make $60 million during its opening weekend, but the film might perform poorly overseas. Accordingly, if the film's budget was $220 million, this figure, even if roughly accurate, does not enable a distributor to scale back its budget prior to the film's production or to move the release date to a more profitable, less competitive date (c.f., the opening weekend performance of Solo: A Star Wars Story).
Foreign tracking carries many of the same limitations and flaws associated with domestic tracking, and these limitations are further compounded by the extensive diversity inherent in movie-going audiences attending films (i.e. what may be an attractive film in one country may not be popular in another country based, for example, on the film's content). For instance, a film pertaining to American football may be less attractive to non-American audiences in foreign countries than it is to domestic audiences. Furthermore, in addition to the multitude of issues associated with domestic tracking, foreign tracking figures are typically not comparable to each other across films—including films released on the same date in different years, films released on different dates within the same year, and even films released on the same date within the same year. The reason for this is that films with extensive foreign roll-outs have varied release strategies, and different countries have different movie-going audience sizes. For instance, a first set of six films (e.g., designated Films 1-6) may open in China, South Korea, Japan, and the United States on the same weekend, while a second set of six films (e.g., designated Films 7-12) may open in the same countries (i.e. China, South Korea, Japan, and the United States) on different weekends, and one nation's audience may respond differently to a film than another nation's audience. By predicting the performance of a subsequently-released film (e.g. designated Film 13) on the basis of the total foreign box office performance of Films 1-12, rather than attempting to forecast the opening weekend of each film immediately prior to its release, there is more data available to use, so the likely total box office performance can be predicted on the basis of similarities in respective film production budgets and release dates. Since film distributors jockey for desirable release dates years in advance, providing a best estimate of the total audience size would allow for fine-tuning of a film's budget and/or for selection of a more beneficial release date well in advance of the film's release, rather than observing, on a day-to-day basis, the outcomes after a film has been released and its release date has been established.
Holidays are another predictive factor often relied upon by film production studios. However, reliance upon holidays for predicting the success of a movie prior to its release also has inherent limitations, drawbacks and disadvantages. The history of the film industry is replete with examples illustrating the limitations associated with the use of holidays as a predictive factor used to determine a film release date. Gut intuition might indicate that films will perform better during certain periods of time when more people have free time to view a film, such as the calendar period from Christmas to New Year's, when the vast majority of adults are off from work and children are out of school, or during the summer. One could reason that a film released during December—in light of Christmas and New Year's holidays falling within the same month (i.e. December)—is likely to perform better than a corresponding film released during October. However, this is not necessarily an accurate assumption. For example, Gravity (released Oct. 4, 2013) made $723 million on a budget of $100 million and The Martian (released Oct. 2, 2015) made $630 million on a budget of $108 million, while Chronicles of Narnia: The Voyage of the Dawn Treader (released Dec. 10, 2010) made $415 million on a budget of $155 million and Tron: Legacy (released Dec. 17, 2010) made $400 million on a budget of $170 million. Such differences are also unlikely to be due to an expanding market over time or fluctuating exchange rates, because, for example, Avatar, released in 2009, made $2.7 billion globally with a December 18th release date.
Although holidays clearly play some role, they do not fully explain the competitive dynamics at play between films released during overlapping periods. For example, as illustrated above, films released in October compete against films released in December. Furthermore, films released to take advantage of the Christmas holiday often make elevated amounts of revenue prior to the actual winter recess (e.g., Star Wars: The Force Awakens made $363 million prior to Dec. 24, 2015). Thus, for example, one would be guessing to say that a film released on Christmas Day (i.e. December 25th) will perform better than a film released earlier in the month (e.g., December 13th).
Accordingly, there is a clear need in the film production/distribution industry for a box office performance prediction method that accounts for cyclical and changing competitive dynamics on a year-to-year basis, and accounts for (using the December time period as an example) the fact that blockbuster films released during December face off in the theaters against so-called “Awards Season” movies that have lower budgets and typically perform worse abroad than they do domestically, as they are primarily produced for domestic audiences and domestic award recognition. This is reflected in the total box office performance of films released on the same/similar dates in the same month in prior years relative to their respective production budgets.
Oftentimes, brand awareness is used as a predictor to guesstimate that a film will perform well (e.g. because “virtually everyone” has heard of it). However, “virtually everyone” has heard of Tarzan, yet the Legend of Tarzan (made with a $180 million production budget and released on Jul. 1, 2016) failed make at least 3-times its production budget. Yet, “virtually everyone” has heard of Harry Potter: Fantastic Beasts and Where to Find Them, directed by the same director (i.e. David Yates) and released by the same distributor (i.e., Warner Brothers) as The Legend of Tarzan, did exactly that (i.e. made at least 3-times its production budget) on an identical production budget of $180 million. It was released on Nov. 18, 2016. This difference is not likely attributable to a greater number of students on vacation from school (i.e. during the Thanksgiving recess) compared with the number of students on vacation from school during the month of July; rather, it is due to the competitive dynamics during the period of its theatrical run—which were similar to those facing the Hunger Games films that debuted on similar dates in November.
Aggregated critical reviews are another film box office performance predictor sometimes relied upon. Although critical reviews may influence the social media buzz for a film, using critical reviews to predict a best estimate of total audience size for a blockbuster film over its entire theatrical run has its own inherent flaws. The Pirates of the Caribbean movie franchise is a case in point. It has been critically panned repeatedly and consistently receives terrible critical reviews. Yet, the Pirates franchise is extremely profitable, with a worldwide audience size of approximately 75+ million ticket buyers. Furthermore, there is no guarantee that the particular critics participating on movie review aggregator websites are representative of the actual audience for a given film. One reason for this is that most movie critics on such sites are native speakers of English whom primarily write for American or British publications, and the internet traffic to a given movie review aggregator website is not necessarily predictive of ticket-buying behavior around the world. Predicting a best estimate of the likely audience size is essentially predicting how many people are likely to purchase tickets over the course of a film's theatrical run (e.g. a less than $531 million worldwide box office return divided by an average ticket price of $10/ticket equals less than 53.1 million ticket purchasers).
Historical patterns have also been used as a predictive factor. Utilizing historical patterns to arrive at a good release date is also a flawed methodology because historical patterns are highly dependent upon competitive dynamics, which are subject to change. For example, one might “predict” that April would be a terrible release period for a given movie because a particular competing movie series (e.g. the Fast and Furious movie series) typically dominates the box office during April. However, this prediction would be completely inaccurate if, during a particular year, there is not a release of a Fast and Furious movie. Furthermore, this prediction would still be inaccurate if there is a release of a Fast and Furious movie, but the release date falls in the middle of April, which could potentially provide space (i.e. for release of the given movie) for the first half of April (i.e. prior to the release date of the competing Fast and Furious movie). As a further example, one could predict that March would be an excellent month to schedule the release of a film based upon an assumption that the March release date will enable the film to avoid the intense competition known to occur during the summer movie release period. However, such an assumption could have disastrous consequences if the competitive dynamics have changed such that March turns out to be crowded with various blockbuster movies all pursuing the same “escape from intense competition elsewhere” strategy. Instead, it would be highly desirable to have access to a more objective, accurate and reliable method for predicting, well in advance of an announced film release date (and preferably during production budgeting), a likely audience size for a particular film relative to the film's budget and the likely competitive dynamics during a proposed film release date. It would be even more beneficial to have access to such a method functioning to provide machine-learning-generated film box office performance predictions based upon identification of stable competitive dynamics patterns. The general concept of incorporating machine learning as a means for generating box office performance predictions, rather than human-calculation based means used in the past, has been proposed. However, the general consensus in film industry is that proposed conventional machine learning technology (e.g. machine learning relying upon neural network algorithms) is not yet accurate enough. Various other box office performance prediction methods (e.g. based upon the number of pre-sold showings, ComScore PreAct measures, and CinemaScore ratings) suffer from similar drawbacks, disadvantages and limitations as the above described known methods.
Accordingly, it would be very highly desirable to provide an improved film box office performance prediction method that overcomes the aforementioned drawbacks, disadvantages, and limitations associated with the various techniques, methods, and other means that have been heretofore used. Preferably, the desired method would incorporate an improved machine learning methodology actually capable of providing reliable estimates of total audience size relative to film production budget on a given film release date, given the likely patterns of competitive dynamics on that particular day in that month of that year, without relying on potentially misleading subjective evaluations, thereby enabling better film production budgeting and release timing decisions at the time of budgeting.